Data Quality


5 Ways Artificial Intelligence Is Transforming CRMs

#artificialintelligence

While 2018 saw the artificial intelligence sales revolution beginning to gain momentum, the applications were limited. In 2018, the percentage increased by a mere 2% to 53% adoption. This year, artificial intelligence will increasingly play a vital role in sales organizations. One of the most profound implications will be in the context of CRMs. CRMs have long struggled to gain the favor of sales professionals.


Data Wrangling Is AI's Big Business Opportunity

#artificialintelligence

Artificial intelligence (AI) is quickly becoming a day-to-day component of software development across the globe. If you've been following the trends at all, you're probably very familiar with the term "algorithm." That's because, to the world's big tech companies like Google, Amazon and Facebook, AI is all about developing and leveraging new AI algorithms to gain deeper insights from the information being collected on and about all of us. However you feel about privacy, the tech giants' emphasis on algorithms has been good for AI and machine learning (ML) businesses in general. Not only are these companies pushing the boundaries of ML, but they're also putting their algorithms out there as open-source products for the world to use.


The Role Of Data In The Age Of Digital Transformation

#artificialintelligence

It might be an understatement to say that today's business environment has become hyper-competitive, and the companies that aren't continuously reinventing their business -- with data at the core -- will end up watching from the sidelines while their market is disrupted. Data technologies, science and processes are rewriting the rules of business and propelling organizations toward digital transformation. Digital transformation, and the radical rethinking of how an enterprise uses technology to meet customer expectations and dramatically affect performance, is happening at a dizzying pace. In fact, IDC predicted that global spending on digital transformation technologies and services was expected to increase by nearly 20% in 2018 to more than $1.1 trillion. At the foundation of the radical rethinking vital to digital transformation is intelligent management of the proliferation of data throughout the enterprise.


The Role Of Data In The Age Of Digital Transformation

#artificialintelligence

It might be an understatement to say that today's business environment has become hyper-competitive, and the companies that aren't continuously reinventing their business -- with data at the core -- will end up watching from the sidelines while their market is disrupted. Data technologies, science and processes are rewriting the rules of business and propelling organizations toward digital transformation. Digital transformation, and the radical rethinking of how an enterprise uses technology to meet customer expectations and dramatically affect performance, is happening at a dizzying pace. In fact, IDC predicted that global spending on digital transformation technologies and services was expected to increase by nearly 20% in 2018 to more than $1.1 trillion. At the foundation of the radical rethinking vital to digital transformation is intelligent management of the proliferation of data throughout the enterprise.


5 Ways Artificial Intelligence Is Transforming CRMs

#artificialintelligence

The effectiveness of artificial intelligence is directly proportional to the accuracy of the data it is fed. Artificial intelligence tools are integral to data cleanliness. By 2025, we will create 180 zettabytes of data each year. Gone are the days when humans can ensure optimal data quality. Artificial intelligence is able to detect irregularities, anomalies, duplicates, and other errors that compromise CRM data and, in turn, customer relationships.


The real big-data problem and why only machine learning can fix it - SiliconANGLE

#artificialintelligence

Why do so many companies still struggle to build a smooth-running pipeline from data to insights? They invest in heavily hyped machine-learning algorithms to analyze data and make business predictions. Then, inevitably, they realize that algorithms aren't magic; if they're fed junk data, their insights won't be stellar. So they employ data scientists that spend 90% of their time washing and folding in a data-cleaning laundromat, leaving just 10% of their time to do the job for which they were hired. What is flawed about this process is that companies only get excited about machine learning for end-of-the-line algorithms; they should apply machine learning just as liberally in the early cleansing stages instead of relying on people to grapple with gargantuan data sets, according to Andy Palmer, co-founder and chief executive officer of Tamr Inc., which helps organizations use machine learning to unify their data silos.


Council Post: The First Steps To Digital Transformation? Get Your Data In Order

#artificialintelligence

Antonio Piraino is Chief Technology Officer at ScienceLogic, where he guides the company's IT management vision and product strategy. Recently, Gartner announced its top 10 strategic technology trends for 2019. It is a nice list, touching on digital transformation trends that range from empowered edge computing to artificial intelligence-driven autonomous things. But while Gartner's trends sound great in annual reports and Forbes articles, operationally, most enterprises aren't properly (or digitally) prepared to adopt these trends. Today's pace of business and the disorderly data that's needed to make sense of it all.


Ditch the data scientists and weaponize your data with AI tech (VB Live)

#artificialintelligence

Join this VB Live webinar to learn about the five biggest mistakes companies make when they bring cutting-edge customer service technology to their workflows, and how to leap over these pitfalls and into real results. Most business leads are aware of the importance of AI, says Michael Butler, head of customer success at Ople, but often don't know how to get started – or if an investment in AI technology is the smartest route to stacking up real ROI. Previously, as director of global ecommerce at VMWare, he was relying entirely on his data science team, Butler says. The team consisted of about 35 people on staff full time, and the problem was that they were slow to produce models and results. For instance, coming up with a model to score customers most likely to buy a new release would take six weeks; when an anniversary sale came along, it would take another six weeks, starting from scratch each time.


Identification In Missing Data Models Represented By Directed Acyclic Graphs

arXiv.org Machine Learning

Missing data is a pervasive problem in data analyses, resulting in datasets that contain censored realizations of a target distribution. Many approaches to inference on the target distribution using censored observed data, rely on missing data models represented as a factorization with respect to a directed acyclic graph. In this paper we consider the identifiability of the target distribution within this class of models, and show that the most general identification strategies proposed so far retain a significant gap in that they fail to identify a wide class of identifiable distributions. To address this gap, we propose a new algorithm that significantly generalizes the types of manipulations used in the ID algorithm, developed in the context of causal inference, in order to obtain identification.


7 Steps to Mastering Data Preparation for Machine Learning with Python -- 2019 Edition

#artificialintelligence

Whatever term you choose, they refer to a roughly related set of pre-modeling data activities in the machine learning, data mining, and data science communities. Data cleansing may be performed interactively with data wrangling tools, or as batch processing through scripting. This may include further munging, data visualization, data aggregation, training a statistical model, as well as many other potential uses. Data munging as a process typically follows a set of general steps which begin with extracting the data in a raw form from the data source, "munging" the raw data using algorithms (e.g. I would say that it is "identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data" in the context of "mapping data from one'raw' form into another..." all the way up to "training a statistical model" which I like to think of data preparation as encompassing, or "everything from data sourcing right up to, but not including, model building."